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Currently, a variety of models are available for predicting the uptake, translocation, and elimination of organic contaminants by plants. These models range from simple deterministic risk assessment screening tools to more complex models that consider physical, chemical, and biological processes in a mechanistic manner. This study evaluates the performance of a range of such models and model types against experimental data sets. Three dynamic, three regression-based, and three steady-state and equilibrium models have been selected for evaluation. These models differ in terms of their scope, methodological approach, and complexity. Data from nine published experiments were used to create scenarios to test model performance. These experiments consider plant contamination via both soil and aerial exposure pathways. A total of 19 different organic chemicals were used in the experiments along with 7 different plant species. Model predictions of chemical concentrations in the relevant plant compartments were compared with the experimentally recorded values. The results indicate that dynamic models offer performance advantages for acute exposure durations and for rapidly changing environmental media. Equilibrium/steady-state and regression-based models perform better for chronic exposure durations, where stable conditions are more likely to exist. The selection of an appropriate plant uptake model will therefore be dependent on the requirements of the assessment, the nature of the environmental media, and the duration of the source term. The results generated by the regression-based models suggest that in their current form these models are unsuitable for evaluating the uptake of organic chemicals from the air into plants.

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